Federated Learning for Industrial Internet of Things in Future Industries

• URL: http://arxiv.org/abs/2105.14659v1
• Date: Mon, 31 May 2021 01:02:59 GMT
• Title: Federated Learning for Industrial Internet of Things in Future Industries
• Authors: Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor
• Abstract summary: The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications. Federated Learning (FL) is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge.
• Score: 106.13524161081355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications where AI techniques require centralized data collection and processing. However, this is not always feasible in realistic scenarios due to the high scalability of modern IIoT networks and growing industrial data confidentiality. Federated Learning (FL), as an emerging collaborative AI approach, is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge while helping protect user privacy. In this article, we provide a detailed overview and discussions of the emerging applications of FL in key IIoT services and applications. A case study is also provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a range of interesting open research topics that need to be addressed for the full realization of FL-IIoT in industries.

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